This paper draws attention to the potential of computational methods in reworking data generated in past qualitative studies. While qualitative inquiries often produce rich data through rigorous and resource-intensive processes, much of this data usually remains unused. In this paper, we first make a general case for secondary analysis of qualitative data by discussing its benefits, distinctions, and epistemological aspects. We then argue for opportunities with computationally intensive secondary analysis, highlighting the possibility of drawing on data assemblages spanning multiple contexts and timeframes to address cross-contextual and longitudinal research phenomena and questions. We propose a scheme to perform computationally intensive secondary analysis and advance ideas on how this approach can help facilitate the development of innovative research designs. Finally, we enumerate some key challenges and ongoing concerns associated with qualitative data sharing and reuse.

Federated Learning (FL) enables deep learning model training across edge devices and protects user privacy by retaining raw data locally. Data heterogeneity in client distributions slows model convergence and leads to plateauing with reduced precision. Clustered FL solutions address this by grouping clients with statistically similar data and training models for each cluster. However, maintaining consistent client similarity within each group becomes challenging when data drifts occur, significantly impacting model accuracy. In this paper, we introduce Fielding, a clustered FL framework that handles data drifts promptly with low overheads. Fielding detects drifts on all clients and performs selective label distribution-based re-clustering to balance cluster optimality and model performance, remaining robust to malicious clients and varied heterogeneity degrees. Our evaluations show that Fielding improves model final accuracy by 1.9%-5.9% and reaches target accuracies 1.16x-2.61x faster.

The bulk kinematics and thermodynamics of hot supernovae-driven galactic winds is critically dependent on both the amount of swept up cool clouds and non-spherical collimated flow geometry. However, accurately parameterizing these physics is difficult because their functional forms are often unknown, and because the coupled non-linear flow equations contain singularities. We show that deep neural networks embedded as individual terms in the governing coupled ordinary differential equations (ODEs) can robustly discover both of these physics, without any prior knowledge of the true function structure, as a supervised learning task. We optimize a loss function based on the Mach number, rather than the explicitly solved-for 3 conserved variables, and apply a penalty term towards near-diverging solutions. The same neural network architecture is used for learning both the hidden mass-loading and surface area expansion rates. This work further highlights the feasibility of neural ODEs as a promising discovery tool with mechanistic interpretability for non-linear inverse problems.

Limited fielding began in 1994 to select U.S. Army National Guard units and complete fielding to all M1 Abrams tank maintenance units started in 1997 and will finish by the end of 1998. The Army estimates that TED will save roughly $10 million a year through improved diagnostic accuracy and reduced waste. The development and fielding of the TED program represents the Army's first successful fielded maintenance system in the area of AI. Several reasons can be given for the success of the TED program: an appropriate domain with proper scope, a close relationship with the expert, extensive user involvement, and others that are discussed in this article.

Machine learning, a form of artificial intelligence, may help improve care for patients with kidney failure. Chronic kidney disease is a life-long condition in which the kidneys can gradually stop working over a period of months or years. A significant number of patients with the condition are either on dialysis or have had a kidney transplant. The findings on how machine learning may improve kidney patient care come from a study that are being presented this week at ASN Kidney Week 2019 that takes place from November 5 – November 10 at the Walter E. Washington Convention Center in Washington. For the study, researcher, Ollie Fielding, and his colleagues deployed a machine learning model to identify patients at risk of requiring kidney replacement therapy, such as dialysis or kidney transplantation.

Washington, DC (November 7, 2019) -- Machine learning, a form of artificial intelligence, may help improve care for patients with kidney failure. The findings come from a study that will be presented at ASN Kidney Week 2019 November 5–November 10 at the Walter E. Washington Convention Center in Washington, DC. For the study, Ollie Fielding (pulseData, in New York) and his colleagues deployed a machine learning model to identify patients at risk of requiring kidney replacement therapy, such as dialysis or kidney transplantation. An electronic health record database of 110,998 patients was used to create a machine learning model to predict progression to kidney failure. The system calculates weekly risk scores for patients, and for those with high risk scores, an alert is sent so that treatment discussions can be made by a multidisciplinary team of clinicians.

Washington, DC (November 7, 2019) -- Machine learning, a form of artificial intelligence, may help improve care for patients with kidney failure. The findings come from a study that will be presented at ASN Kidney Week 2019 November 5-November 10 at the Walter E. Washington Convention Center in Washington, DC. For the study, Ollie Fielding (pulseData, in New York) and his colleagues deployed a machine learning model to identify patients at risk of requiring kidney replacement therapy, such as dialysis or kidney transplantation. An electronic health record database of 110,998 patients was used to create a machine learning model to predict progression to kidney failure. The system calculates weekly risk scores for patients, and for those with high risk scores, an alert is sent so that treatment discussions can be made by a multidisciplinary team of clinicians.

Breakthroughs in AI happen with a team of people working behind the scenes to make it all possible. If AI development were a sport, it'd be closer to baseball than boxing. Headlines might make it seem like AI breakthroughs happen with a big knockout punch, but the reality is more akin to a baseball team grinding through a 162-game season. It's a process that involves having the right people in place over a long stretch, and fielding the best team is essential for success. The root cause for this can be found in the data.

While on my way from the beautiful snows of Davos to the warmth at AAAI19 in Honolulu, I've been reflecting about events and discussions at the 2019 World Economic Forum (WEF19). I found that energy and passion were high at WEF19. I especially enjoyed my 1:1 conversations with leaders from industry, government, academia, and civil society. Advances in AI and their influences seemed to pervade conversations and presentations at WEF19. It was inspiring to see the great enthusiasm about advances in AI. However, it seemed that many folks' understandings, expectations, and concerns about AI at WEF19 have come through tech press writings and popular books.

The Irish Data Protection Commission has opened a formal investigation into a data breach that affected nearly 50m Facebook accounts, which could result in a fine of up to $1.63bn. The breach, which was discovered by Facebook engineers on Tuesday 24 September, gave hackers the ability to take over users' accounts. It was patched on Thursday, the company said. "The investigation will examine Facebook's compliance with its obligation under the General Data Protection Regulation (GDPR) to implement appropriate technical and organisational measures to ensure the security and safeguarding of the personal data it processes," the commission said in a statement on Wednesday. The commission regulates Facebook's adherence to GDPR, a European law that strengthens the privacy protections of individuals and introduces harsh penalties for companies that fail to protect user data.